Abstract

As part of Big Data Challenge on Signalized Intersections 2019, a high-resolution intersection-based signal and flow detection data was observed, processed, and presented in here. Macro/Meso-scopic level analysis was performed. Also, an open-source, R, was used from the beginning to end since R is one of the powerful tools for big-data processing and analysis. Finally, simple vehicle delay curbs and signal time distributions for each intersection were observed.

I. Introduction

To improve traffic performance measures, the Utah Department of Transportation agreed to release a dataset consisting 22 signalized intersections along two major corridors on Salt Lake City, Utah, United States of America since January 2018 to December 2018. The given high resolution data set, has recorded with 0.1-second time interval, incorporating variety of information with a quite simple format, such as vehicle and pedestrian detections and signal phase changes including ring and barriers even though recorded data sizes are relatively small compared with traditional detector types, such as loop-detectors or blue-tooth-detectors. This key data achieving and recoding innovation are embraced by an effective utilization of series of lookup tables as database fashion. Figure 1 below describes intersection deployment used for this study. Each intersection identified as its unique number along with two major corridors of Salt Lake City, 300 West and 700 East and each intersection has diverse capacities with traffic patterns.

Figure 1. 300 West and 700 East of Salt Lake City

II. Data Structure

Each observation from detector has recorded to four categories: SignalID, Time, Event Code, and Parameter. SignalID indicates the location of detector observation and this information becomes to the key information for approach, detectors, movement, and lane information. Event Code is another key information whether a detection was about vehicle movement or signal changes. This information is highly correlated with Parameter which can explain more details of each Event Code, such as phase information in case of signal detection, detection details in case of vehicle detection. Below figure describing the overall data architecture for the given high-resolution detector data. Figure 2. A general view for data structure

III. Pre-Data Processing

Data pre-processing was performed by splitting signal and detector information for each detector record and allocates appropriate information by belonging event code and event for signal/ DET channel for detector.

a. Signal data processing: data reproducing

It was observed that seven out of several informative records could tell backbone of the signal record story. Which are Phase green start, minimum green end, green time termination and yellow time start, yellow time termination and red time start, and red time termination. Although ring and barrier information were recorded, these seven records by phase were more consistently observable. Table 1. Key signal information that used for this study

b. Detector data processing: data reproducing based on lookup table

This information contains quite specific vehicle movements by time, lane and occupancy. However, it was also observed that some of event data information was missing or not existed from the given lookup table. Therefore, this study focuses more on snapshot for operational performance as messo- and macro-scopic level instead of microscopic level approach. Since whole month or whole year data might cause confuse regardless of analysis method, this study also picked one day of week, i.e., Tuesday, out of week and Figure 3 shows average vehicle observations by day of week across two corridors. For example, in Tuesday, about 17% of traffics were observed other day of week such as Wednesday (~14%), Monday (~16%). Note that you can see actual numbers by hovering mouse and zoom-in or zoom-out by dragging a mouse on the screen.

Figure 3. Average observations by day of week Furthermore, this study focuses 3000West corridor during 12 months on every Tuesday since the same analysis and approach can be applied easily to the other later on.

IV. What happens in Tuesday?

a. Signal allocation distribution

Below figures show signal phase distributions for each intersection on every Tuesday from January to December. The phase numbers are referred by NEMA Phase # Convention at UDOT. As the series of these snapshots are showing, 700-East corridor has more consistent signal distributions for the major direction, i.e., North bound and South bound, whereas 300-West corridor shows relatively diverse signal distributions. For example, some of intersections on 300-West corridor between 7122 and 7124 have almost major bound focused signal distribution, meanwhile from 7125 to southbound have diverse signal distributions due to other approaching corridors, such as North Temple, 400-South corridor. Figure 4. Signal distribution snapshot by phases by month

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b. b. Vehicle delay distribution

Based on the detector observation, some of informative delay records can be observable by time of day, month of year, and so on. Some of findings are presented in here. As Figure 5a and 5b are showing, Intersection 7123 has relatively higher delay along with 7241 and 7147. Presumabley compliated signal phases on those intersections might cause additional delay to passing vehicles

Figure 5a. Vehicle delay distribution snapshot by phases by month (NB)
Figure 5b. Vehicle delay distribution snapshot by phases by month (SB)